NDBC buoys (tabledap -> DataFrame)¶

A tabledap dataset returns record tables as a pandas.DataFrame. Here we pull a day of NDBC standard-meteorological buoy water-temperature observations off the U.S. west coast.

Setup + request¶

OUTPUT_KIND is tabular, so download() returns a frame (also written to CSV). aggregate= would be rejected for a table.

In [ ]:

import tempfile
from pathlib import Path

from earthlens.core import EarthLens

out_dir = Path(tempfile.mkdtemp(prefix='earthlens-erddap-ndbc-'))
df = EarthLens(
    data_source='erddap',
    dataset='cwwcNDBCMet',
    variables=['station', 'time', 'wtmp'],
    start='2023-01-01',
    end='2023-01-01',
    lat_lim=[36.0, 38.0],
    lon_lim=[-124.0, -122.0],
    path=out_dir,
).download()

print('shape  :', df.shape)
print('columns:', list(df.columns))

import tempfile from pathlib import Path from earthlens.core import EarthLens out_dir = Path(tempfile.mkdtemp(prefix='earthlens-erddap-ndbc-')) df = EarthLens( data_source='erddap', dataset='cwwcNDBCMet', variables=['station', 'time', 'wtmp'], start='2023-01-01', end='2023-01-01', lat_lim=[36.0, 38.0], lon_lim=[-124.0, -122.0], path=out_dir, ).download() print('shape :', df.shape) print('columns:', list(df.columns))

Peek at the records¶

ERDDAP appends units to the column names (e.g. wtmp (degree_C)).

In [ ]:

df.head()

df.head()

Per-station water temperature¶

In [ ]:

wtmp_col = [c for c in df.columns if c.startswith('wtmp')][0]
summary = (
    df.dropna(subset=[wtmp_col])
    .groupby('station')[wtmp_col]
    .agg(['count', 'mean', 'min', 'max'])
    .round(2)
)
print(f'{len(summary)} station(s) reported water temperature:')
summary

wtmp_col = [c for c in df.columns if c.startswith('wtmp')][0] summary = ( df.dropna(subset=[wtmp_col]) .groupby('station')[wtmp_col] .agg(['count', 'mean', 'min', 'max']) .round(2) ) print(f'{len(summary)} station(s) reported water temperature:') summary